This invention relates in general to neural networks and, more particularly, to a circuit and method of enhancing the contrast of an input signal vector with the recognition properties of an artificial neural network.
Neural networks are well known in the art for contrast enhancement and recovery of distorted signals in the presence of noise. When a noisy or corrupt input signal vector is applied at the synaptic inputs of a neural network, for example a pre-trained Kohonen feature map, the neuron, or group of neurons, having synapses most closely matched to the elements of the input signal vector maintains the highest level of output signal activity relative to the other neurons in the feature map. Even though one or more elements of the input signal vector are corrupt or missing, the feature map still recognizes the data pattern. Thus, neural networks generally possess the property of averaging-out the noise and missing data such that the closest transformation of an ideal input signal vector may still be realized at the output of the feature map. Of course, the cleaner the input signal vector, the higher the output signal activity of the matching neuron(s) and the more confidence in the recognition level of the neural network.
Most neural networks provide a non-recursive, single pass of the input signal vector through the neurons in a particular feature map layer. The neural network accepts the input signal vector as the best available information from which to decode and recognize the associated information. Neural networks in the prior art generally lack the feature of using the inherent recognition properties of the network to pre-condition and clean up the input signal vector by removing noise and identifying missing or corrupt elements.
Another limitation with conventional neural networks is that self-organized neural networks traditionally cannot do supervised training. However, they have other advantages including rapid learning and a simple local learning rule which would be useful for supervised learning.
Hence, a need exits for an improved neural network having increased resolution of the input signal vector by reducing the noise content thereof by replacing missing or corrupt elements of information. It would be advantageous to optionally select supervised training only, self-organized training only, or some combination thereof in the same neural network.